Part II - (Explanatory analysis of Loan Data)¶

by (Williams Miebi Gbeneowei)¶

Investigation Overview:¶

This presentation is meant to show factors that affects the outcome of a loan's status and also display the effect of loan term on annual percentage rate(APR) of the borrower.

Dataset Overview¶

DATA SET:¶

The provided data set consists of several key performance indicators such as borrowerAPR,borrower rate,income range, prosper rating, etc of 113937 loans. And over 80 columns. The key performance indicators are what determines if a prospective borrower will be granted a loan and how many percent interest will charged for the loan and the time duration that will be granted for payment of the loan.

In [1]:
#Importation of all the needed libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import nbconvert
%matplotlib inline
In [2]:
#loading the data sets
loans = pd.read_csv('ProsperLoanData.csv')
In [3]:
#Assessment of the data
loans.head()
loans.info()
loans.describe
loans.shape
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113937 non-null  object 
 1   ListingNumber                        113937 non-null  int64  
 2   ListingCreationDate                  113937 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113937 non-null  int64  
 5   LoanStatus                           113937 non-null  object 
 6   ClosedDate                           55089 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113937 non-null  float64
 9   LenderYield                          113937 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113937 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113937 non-null  bool   
 22  CurrentlyInGroup                     113937 non-null  bool   
 23  GroupKey                             13341 non-null   object 
 24  DateCreditPulled                     113937 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113937 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113937 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105383 non-null  float64
 47  IncomeRange                          113937 non-null  object 
 48  IncomeVerifiable                     113937 non-null  bool   
 49  StatedMonthlyIncome                  113937 non-null  float64
 50  LoanKey                              113937 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113937 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113937 non-null  int64  
 62  LoanNumber                           113937 non-null  int64  
 63  LoanOriginalAmount                   113937 non-null  int64  
 64  LoanOriginationDate                  113937 non-null  object 
 65  LoanOriginationQuarter               113937 non-null  object 
 66  MemberKey                            113937 non-null  object 
 67  MonthlyLoanPayment                   113937 non-null  float64
 68  LP_CustomerPayments                  113937 non-null  float64
 69  LP_CustomerPrincipalPayments         113937 non-null  float64
 70  LP_InterestandFees                   113937 non-null  float64
 71  LP_ServiceFees                       113937 non-null  float64
 72  LP_CollectionFees                    113937 non-null  float64
 73  LP_GrossPrincipalLoss                113937 non-null  float64
 74  LP_NetPrincipalLoss                  113937 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64
 76  PercentFunded                        113937 non-null  float64
 77  Recommendations                      113937 non-null  int64  
 78  InvestmentFromFriendsCount           113937 non-null  int64  
 79  InvestmentFromFriendsAmount          113937 non-null  float64
 80  Investors                            113937 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.1+ MB
Out[3]:
(113937, 81)

There is a need to allow jupyter notes to allow us view all the columns visually to get an idea of the data. To enable us view that we will be doing a little resetting below.

In [4]:
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows',None)
In [5]:
loans.head()
loans.IncomeVerifiable.value_counts()
Out[5]:
True     105268
False      8669
Name: IncomeVerifiable, dtype: int64
In [6]:
loans.LoanStatus.isnull().sum()
Out[6]:
0
In [7]:
loans.CreditGrade.isnull().sum()
Out[7]:
84984

As seen above from the first assessment, there are a whooping 81 columns and over 110,000 rows of data, We will not be needing all those columns, we will keep only columns relevant to the insights to be gotten from the data and possibly delete some rows of data as well if neccessary.¶

We can either create a subset of the data to be used or completely drop irrelevant columns I think dropping the columns will be better to reduce the load and increase the processing speed. But it's much easier and saves time to use a subset of the data.

After proper thought and scrutiny of the data set based on possible insights that can be gotten from the data.The columns in the cell below will be used for the analysis

In [8]:
#To display the columns
loans.columns
Out[8]:
Index(['ListingKey', 'ListingNumber', 'ListingCreationDate', 'CreditGrade',
       'Term', 'LoanStatus', 'ClosedDate', 'BorrowerAPR', 'BorrowerRate',
       'LenderYield', 'EstimatedEffectiveYield', 'EstimatedLoss',
       'EstimatedReturn', 'ProsperRating (numeric)', 'ProsperRating (Alpha)',
       'ProsperScore', 'ListingCategory (numeric)', 'BorrowerState',
       'Occupation', 'EmploymentStatus', 'EmploymentStatusDuration',
       'IsBorrowerHomeowner', 'CurrentlyInGroup', 'GroupKey',
       'DateCreditPulled', 'CreditScoreRangeLower', 'CreditScoreRangeUpper',
       'FirstRecordedCreditLine', 'CurrentCreditLines', 'OpenCreditLines',
       'TotalCreditLinespast7years', 'OpenRevolvingAccounts',
       'OpenRevolvingMonthlyPayment', 'InquiriesLast6Months', 'TotalInquiries',
       'CurrentDelinquencies', 'AmountDelinquent', 'DelinquenciesLast7Years',
       'PublicRecordsLast10Years', 'PublicRecordsLast12Months',
       'RevolvingCreditBalance', 'BankcardUtilization',
       'AvailableBankcardCredit', 'TotalTrades',
       'TradesNeverDelinquent (percentage)', 'TradesOpenedLast6Months',
       'DebtToIncomeRatio', 'IncomeRange', 'IncomeVerifiable',
       'StatedMonthlyIncome', 'LoanKey', 'TotalProsperLoans',
       'TotalProsperPaymentsBilled', 'OnTimeProsperPayments',
       'ProsperPaymentsLessThanOneMonthLate',
       'ProsperPaymentsOneMonthPlusLate', 'ProsperPrincipalBorrowed',
       'ProsperPrincipalOutstanding', 'ScorexChangeAtTimeOfListing',
       'LoanCurrentDaysDelinquent', 'LoanFirstDefaultedCycleNumber',
       'LoanMonthsSinceOrigination', 'LoanNumber', 'LoanOriginalAmount',
       'LoanOriginationDate', 'LoanOriginationQuarter', 'MemberKey',
       'MonthlyLoanPayment', 'LP_CustomerPayments',
       'LP_CustomerPrincipalPayments', 'LP_InterestandFees', 'LP_ServiceFees',
       'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss',
       'LP_NonPrincipalRecoverypayments', 'PercentFunded', 'Recommendations',
       'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount',
       'Investors'],
      dtype='object')
In [9]:
# subsetting relevant columns
useful_cols =['EstimatedReturn','LoanStatus','StatedMonthlyIncome','IncomeRange','DelinquenciesLast7Years','Term','AmountDelinquent',
              'ProsperScore','CreditGrade',
             'EmploymentStatus','EmploymentStatusDuration','IsBorrowerHomeowner','CreditScoreRangeLower',
              'CreditScoreRangeUpper',
             'CurrentCreditLines','OpenCreditLines','RevolvingCreditBalance',
              'DebtToIncomeRatio','BorrowerAPR','BorrowerRate',
              'AvailableBankcardCredit','ProsperRating (Alpha)','ProsperRating (numeric)','LoanOriginalAmount']
factors= loans[useful_cols]
In [10]:
#sum of rows with null borrower APR 
factors.BorrowerAPR.isnull().sum()
Out[10]:
25
In [11]:
#displaying the rows with null values for borrower APR
null_APR = pd.isnull(factors["BorrowerAPR"])
factors[null_APR]
Out[11]:
EstimatedReturn LoanStatus StatedMonthlyIncome IncomeRange DelinquenciesLast7Years Term AmountDelinquent ProsperScore CreditGrade EmploymentStatus EmploymentStatusDuration IsBorrowerHomeowner CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (Alpha) ProsperRating (numeric) LoanOriginalAmount
2543 NaN Completed 8333.333333 Not displayed NaN 36 NaN NaN HR NaN NaN False NaN NaN NaN NaN NaN 0.02685 NaN 0.10000 NaN NaN NaN 4200
14632 NaN Completed 4166.666667 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.09031 NaN 0.06500 NaN NaN NaN 2620
25166 NaN Completed 9166.666667 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.04120 NaN 0.04350 NaN NaN NaN 1000
25330 NaN Completed 7083.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.07707 NaN 0.06395 NaN NaN NaN 1500
29773 NaN Completed 8333.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.01923 NaN 0.06000 NaN NaN NaN 3000
39870 NaN Completed 3333.333333 Not displayed NaN 36 NaN NaN C NaN NaN False NaN NaN NaN NaN NaN 0.29069 NaN 0.12000 NaN NaN NaN 15000
43587 NaN Completed 18333.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.01510 NaN 0.08500 NaN NaN NaN 5000
62297 NaN Completed 10000.000000 Not displayed NaN 36 NaN NaN E NaN NaN False NaN NaN NaN NaN NaN 0.01783 NaN 0.17000 NaN NaN NaN 1000
71280 NaN Completed 7083.333333 Not displayed NaN 36 NaN NaN C NaN NaN False NaN NaN NaN NaN NaN 0.37002 NaN 0.09000 NaN NaN NaN 10000
71875 NaN Completed 4166.666667 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.02343 NaN 0.07000 NaN NaN NaN 3000
73945 NaN Completed 29166.666667 Not displayed NaN 36 NaN NaN HR NaN NaN False NaN NaN NaN NaN NaN 0.01234 NaN 0.25000 NaN NaN NaN 2500
74114 NaN Completed 9458.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.01115 NaN 0.06000 NaN NaN NaN 1000
75710 NaN Completed 6666.666667 Not displayed NaN 36 NaN NaN C NaN NaN False NaN NaN NaN NaN NaN 0.09451 NaN 0.10000 NaN NaN NaN 4000
78414 NaN Completed 8333.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.01289 NaN 0.13000 NaN NaN NaN 2000
80369 NaN Completed 4166.666667 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.09802 NaN 0.06500 NaN NaN NaN 2689
81848 NaN Completed 1083.333333 Not displayed NaN 36 NaN NaN D NaN NaN False NaN NaN NaN NaN NaN 0.38380 NaN 0.13000 NaN NaN NaN 4000
90598 NaN Completed 16666.666667 Not displayed NaN 36 NaN NaN B NaN NaN False NaN NaN NaN NaN NaN 0.01051 NaN 0.12000 NaN NaN NaN 1000
92516 NaN Completed 12500.000000 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.04569 NaN 0.07000 NaN NaN NaN 3500
103509 NaN Completed 12500.000000 Not displayed NaN 36 NaN NaN A NaN NaN False NaN NaN NaN NaN NaN 0.07015 NaN 0.09450 NaN NaN NaN 8998
105925 NaN Completed 6666.666667 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.02743 NaN 0.08500 NaN NaN NaN 5000
106465 NaN Completed 4166.666667 Not displayed NaN 36 NaN NaN NC NaN NaN False NaN NaN NaN NaN NaN 0.03826 NaN 0.09000 NaN NaN NaN 4800
106735 NaN Completed 14583.333333 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.01747 NaN 0.05950 NaN NaN NaN 1500
109027 NaN Completed 25000.000000 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.02217 NaN 0.06000 NaN NaN NaN 3000
111607 NaN Completed 16666.666667 Not displayed NaN 36 NaN NaN B NaN NaN False NaN NaN NaN NaN NaN 0.01404 NaN 0.06500 NaN NaN NaN 3000
113921 NaN Completed 12500.000000 Not displayed NaN 36 NaN NaN AA NaN NaN False NaN NaN NaN NaN NaN 0.23284 NaN 0.04000 NaN NaN NaN 1000
In [12]:
#dropping rows with null values for Borrower APR
factors = factors[~factors.BorrowerAPR.isna()]
In [13]:
#testing
factors.BorrowerAPR.isnull().sum()
Out[13]:
0
In [14]:
factors.head()
Out[14]:
EstimatedReturn LoanStatus StatedMonthlyIncome IncomeRange DelinquenciesLast7Years Term AmountDelinquent ProsperScore CreditGrade EmploymentStatus EmploymentStatusDuration IsBorrowerHomeowner CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (Alpha) ProsperRating (numeric) LoanOriginalAmount
0 NaN Completed 3083.333333 $25,000-49,999 4.0 36 472.0 NaN C Self-employed 2.0 True 640.0 659.0 5.0 4.0 0.0 0.17 0.16516 0.1580 1500.0 NaN NaN 9425
1 0.05470 Current 6125.000000 $50,000-74,999 0.0 36 0.0 7.0 NaN Employed 44.0 False 680.0 699.0 14.0 14.0 3989.0 0.18 0.12016 0.0920 10266.0 A 6.0 10000
2 NaN Completed 2083.333333 Not displayed 0.0 36 NaN NaN HR Not available NaN False 480.0 499.0 NaN NaN NaN 0.06 0.28269 0.2750 NaN NaN NaN 3001
3 0.06000 Current 2875.000000 $25,000-49,999 14.0 36 10056.0 9.0 NaN Employed 113.0 True 800.0 819.0 5.0 5.0 1444.0 0.15 0.12528 0.0974 30754.0 A 6.0 10000
4 0.09066 Current 9583.333333 $100,000+ 0.0 36 0.0 4.0 NaN Employed 44.0 True 680.0 699.0 19.0 19.0 6193.0 0.26 0.24614 0.2085 695.0 D 3.0 15000
In [15]:
#summary statistics of the numerical variables
factors.describe()
Out[15]:
EstimatedReturn StatedMonthlyIncome DelinquenciesLast7Years Term AmountDelinquent ProsperScore EmploymentStatusDuration CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (numeric) LoanOriginalAmount
count 84853.000000 1.139120e+05 112947.000000 113912.000000 106315.000000 84853.000000 106312.000000 113346.000000 113346.000000 106333.000000 106333.000000 1.063330e+05 105358.000000 113912.000000 113912.000000 106393.000000 84853.000000 113912.000000
mean 0.096068 5.606973e+03 4.154984 40.831308 984.507059 5.950067 96.071582 685.567731 704.567731 10.317192 9.260164 1.759871e+04 0.275993 0.218828 0.192786 11210.225447 4.072243 8338.015661
std 0.030403 7.478338e+03 10.160216 10.437112 7158.270157 2.376501 94.480605 66.458275 66.458275 5.457866 5.022644 3.293640e+04 0.551814 0.080364 0.074809 19818.361309 1.673227 6245.940592
min -0.182700 0.000000e+00 0.000000 12.000000 0.000000 1.000000 0.000000 0.000000 19.000000 0.000000 0.000000 0.000000e+00 0.000000 0.006530 0.000000 0.000000 1.000000 1000.000000
25% 0.074080 3.200000e+03 0.000000 36.000000 0.000000 4.000000 26.000000 660.000000 679.000000 7.000000 6.000000 3.121000e+03 0.140000 0.156290 0.134000 880.000000 3.000000 4000.000000
50% 0.091700 4.666667e+03 0.000000 36.000000 0.000000 6.000000 67.000000 680.000000 699.000000 10.000000 9.000000 8.549000e+03 0.220000 0.209760 0.184000 4100.000000 4.000000 6500.000000
75% 0.116600 6.817083e+03 3.000000 36.000000 0.000000 8.000000 137.000000 720.000000 739.000000 13.000000 12.000000 1.952100e+04 0.320000 0.283810 0.250000 13180.000000 5.000000 12000.000000
max 0.283700 1.750003e+06 99.000000 60.000000 463881.000000 11.000000 755.000000 880.000000 899.000000 59.000000 54.000000 1.435667e+06 10.010000 0.512290 0.497500 646285.000000 7.000000 35000.000000

Univariate Exploration¶

Univariate plots¶

Borrower APR and Borrower Rate.¶

In [16]:
#use of histogram plots to show the distribution of borrowerAPR
plt.figure(figsize =[10,8])
bins= np.arange(0,factors.BorrowerAPR.max()+ 0.005,0.01)
plt.title("BorrowerAPR distribution across counts")
plt.hist(factors['BorrowerAPR'],bins = bins);
In [17]:
fig, ax = plt.subplots(nrows=2, figsize = [10,10])
default_color = sb.color_palette()[0]
sb.histplot(data = factors,x = 'BorrowerAPR',ax = ax[0]).set_title('BorrowerAPR distribution across counts')
plt.ylim(0,7500)
sb.histplot(data = factors,x = 'BorrowerRate',ax = ax[1]).set_title('Borrower Rate distribution across counts')
Out[17]:
Text(0.5, 1.0, 'Borrower Rate distribution across counts')

Observation:¶

The scale used for plotting this histogram is 0.005 per bin on the x-axis. By just looking at the plot,it can be seen that the borrowerAPR score with highest count is 0.36, the borrowerAPR scores ranges from as low as below 0.0045 to above 0.4. There are'nt many borrower APR scores above 0.4.There happens to be alot of borrower APR scores with around 0.3,0.2 and 0.175.

Income Range¶

A histogram of income Range will be plotted as well to get an idea of the economic class of people that collect loans.

In [18]:
#seaborn is used again to give a more detailed distribution and summary of IncomeRange
sb.histplot(data = factors,y = 'IncomeRange').set_title('Income Range Count')
plt.xticks(
    rotation=45, 
    horizontalalignment='right',
    fontweight='light',
    fontsize='small');

The income range taking the most loans are people earning from $25000-49,999, followed up closely by $50,000-74999 earners

Original Loan Amount¶

In [19]:
#setting the dimensions
fig, ax = plt.subplots(figsize=(10, 6))
#drawing the plot
sb.histplot(data = factors , x ='LoanOriginalAmount',binwidth =500).set_title('Count of Original loan Amount')
plt.xlim(0,36000)
plt.show()

One of the bin represents $500.The lowest loan amount issued is $1500 and the highest issued is $35000.The most issued original loan amount is $4000, with over 14000 counts.

Distribution of credit score¶

There is no column with a credit score, but there are two columns with credit score ranges, we will have to create a new column that will be the mean of both columns in other to get a credit score

In [20]:
#credit score creation using creditscore range upper and creditscore range lower.
#factors['creditscore'] = (factors["CreditScoreRangeUpper"]+factors["CreditScoreLower"]).mean(axis =1)
factors["creditscore"] = factors[["CreditScoreRangeUpper", "CreditScoreRangeLower"]].mean(axis=1)
In [21]:
factors.creditscore.value_counts()
plt.figure(figsize=(15,7))
sb.countplot(data= factors, x= "creditscore").set_title("Distribution of credit Score")
plt.xticks(rotation = 90);

OBSERVATION¶

The distribution of the creditscores are skewed to the right. With 689.5 as the most common creditscore in the data set. The lowest credit score is 9.5, it's an outlier and most likely to be some sort of error.The outlier will be dropped because,credit score cannot be that low. The highest credit score is 889.5 and it's the least common credit score in the data set.

In [22]:
#creating a subset and dropping such rows of data.
credit_score_high = factors[(factors['creditscore']==9.5)].index
credit_score_high
Out[22]:
Int64Index([   794,    912,   1685,   4290,   4371,   4430,   4672,   5684,
              6877,   7739,
            ...
            105032, 106897, 108762, 109060, 109468, 109522, 109855, 110290,
            111624, 113848],
           dtype='int64', length=133)
In [23]:
#dropping the rows
factors.drop(credit_score_high,inplace = True)
In [24]:
#checking the distribution after removing the outlier
factors.creditscore.value_counts()
plt.figure(figsize=(15,7))
sb.countplot(data= factors, x= "creditscore").set_title("Distribution of credit Score")
plt.xticks(rotation = 90);

Stated Monthly Income¶

In [25]:
#distribution of stated Monthly income
plt.figure(figsize=(10,7))
bins_smi = np.arange(0, 50000, 500)
plt.hist(data = factors, x = 'StatedMonthlyIncome', bins=bins_smi);
plt.title('Distribution of borrowers Stated Monthly Income')
plt.xlim(0,45000)
Out[25]:
(0.0, 45000.0)

OBSERVATION¶

A majority of the monthly income of borrowers falls within 0-10,000 dollars monthly income.There are few outliers earning above 25000 dollars per month.

Loan Term¶

In [26]:
factors.Term.value_counts()
Out[26]:
36    87620
60    24545
12     1614
Name: Term, dtype: int64

Employment Status,Loanterm and ProsperRating¶

In [27]:
# Convert ProsperRating and Employment status into ordered categorical types
rate_order = ['HR','E','D','C','B','A','AA']
ordered_var = pd.api.types.CategoricalDtype(ordered = True,
                                    categories = rate_order)
factors['ProsperRating (Alpha)'] = factors['ProsperRating (Alpha)'].astype(ordered_var)

emp_order = ['Employed','Self-employed','Full-time','Part-time','Retired','Other','Not employed', 'Not available']
ordered_var = pd.api.types.CategoricalDtype(ordered = True,
                                    categories = emp_order)
factors['EmploymentStatus'] = factors['EmploymentStatus'].astype(ordered_var)
In [28]:
fig, ax = plt.subplots(nrows=3, figsize = [8,8])
default_color = sb.color_palette()[0]
type_order = factors['Term'].value_counts().index
sb.countplot(data = factors, x = 'Term', color = default_color, order=type_order, ax = ax[0]).set_title('Count of Loan Duration')
sb.countplot(data = factors, x = 'ProsperRating (Alpha)', color = default_color, ax = ax[1]).set_title('Count of Prosper rating ')
sb.countplot(data = factors, x = 'EmploymentStatus', color = default_color, ax = ax[2]).set_title('count of Employment Status');
plt.xticks(rotation=30);
plt.subplots_adjust(left=0.1,
                    bottom=0.1,
                    right=0.9,
                    top=0.9,
                    wspace=0.4,
                    hspace=0.4)

Observation:¶

Employment Status column:¶

The most people being issued loan as can be seen from the plot above is the people under the employed category. Followed next by the Fulltime category.Unemployed category is one of the least issued amongst the part_time and retired categories.

Prosper rating:¶

The category with the highest count for prosper rating is the C category and the least category is the AA category.

Term:¶

A majority of the loans were issued for a period of 36months and the least duartion for loan term is 12months.

Prosper Rating¶

A custom risk score built using historical Prosper data. The score ranges from 1-10, with 10 being the best, or lowest risk score. Applicable for loans originated after July 2009.

In [29]:
default_color = sb.color_palette()[3]
sb.countplot(data = factors, x = 'ProsperScore', color = default_color).set_title('Count of Prosper Score ')
Out[29]:
Text(0.5, 1.0, 'Count of Prosper Score ')

OBSERVATION¶

A majority of the borrowers have a propser score of within 4.0 to 9.0. The prosper score ranges from 1.0 to 10.0, but we can see some borrowers scoring up to 11.0, these must be an error that must be fixed by dropping rows with 11.0

In [30]:
factors.query('ProsperScore > 10')
Out[30]:
EstimatedReturn LoanStatus StatedMonthlyIncome IncomeRange DelinquenciesLast7Years Term AmountDelinquent ProsperScore CreditGrade EmploymentStatus EmploymentStatusDuration IsBorrowerHomeowner CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (Alpha) ProsperRating (numeric) LoanOriginalAmount creditscore
9 0.04231 Current 3333.333333 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 269.0 True 820.0 839.0 17.0 16.0 9906.0 0.25 0.07620 0.0629 77696.0 AA 7.0 10000 829.5
69 0.06499 Current 2700.000000 $25,000-49,999 0.0 60 0.0 11.0 NaN Self-employed 12.0 True 700.0 719.0 5.0 5.0 19961.0 NaN 0.13636 0.1139 5039.0 A 6.0 9000 709.5
158 0.04929 Current 6666.666667 $75,000-99,999 5.0 36 15599.0 11.0 NaN Employed 22.0 False 700.0 719.0 9.0 8.0 2564.0 0.05 0.09469 0.0809 14737.0 AA 7.0 5500 709.5
224 0.06388 Current 33333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 114.0 True 720.0 739.0 16.0 16.0 55769.0 0.11 0.13799 0.1099 103931.0 A 6.0 30000 729.5
236 0.04261 Current 4083.333333 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 105.0 True 820.0 839.0 11.0 11.0 4242.0 0.14 0.06726 0.0605 62608.0 AA 7.0 9000 829.5
277 0.07192 Current 6833.333333 $75,000-99,999 0.0 60 0.0 11.0 NaN Full-time 154.0 False 680.0 699.0 10.0 8.0 23082.0 0.19 0.15868 0.1355 29368.0 B 5.0 20000 689.5
476 0.06491 Current 13083.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 27.0 False 720.0 739.0 25.0 24.0 33126.0 0.29 0.14528 0.1224 4353.0 B 5.0 15000 729.5
491 0.07463 Current 8166.666667 $75,000-99,999 0.0 60 0.0 11.0 NaN Employed 117.0 True 680.0 699.0 8.0 6.0 2748.0 0.18 0.17327 0.1500 4328.0 B 5.0 25000 689.5
532 0.06388 Current 10833.333333 $100,000+ 0.0 36 0.0 11.0 NaN Other 0.0 True 680.0 699.0 17.0 15.0 37603.0 0.15 0.13799 0.1099 136872.0 A 6.0 10000 689.5
554 0.04929 Current 15000.000000 $100,000+ 0.0 60 0.0 11.0 NaN Employed 80.0 True 820.0 839.0 10.0 10.0 689.0 0.04 0.09367 0.0809 59911.0 AA 7.0 20000 829.5
633 0.05879 Current 15000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 21.0 True 740.0 759.0 20.0 20.0 46429.0 0.22 0.12691 0.0990 86307.0 A 6.0 30000 749.5
690 0.06663 Current 4500.000000 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 133.0 True 720.0 739.0 19.0 16.0 14575.0 0.25 0.13227 0.1099 11800.0 A 6.0 15000 729.5
765 0.07077 Current 8458.250000 $100,000+ 0.0 60 0.0 11.0 NaN Employed 210.0 True 740.0 759.0 18.0 18.0 32325.0 0.41 0.15425 0.1314 80375.0 B 5.0 15000 749.5
771 0.05516 Current 9333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 166.0 True 700.0 719.0 13.0 13.0 50301.0 0.21 0.10038 0.0869 60723.0 AA 7.0 19000 709.5
854 0.06910 Current 5416.666667 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 20.0 False 780.0 799.0 11.0 11.0 5222.0 0.48 0.14658 0.1239 32378.0 A 6.0 15000 789.5
890 0.07077 Current 14333.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 96.0 True 700.0 719.0 12.0 11.0 113816.0 0.12 0.15425 0.1314 23727.0 B 5.0 25000 709.5
901 0.06499 Current 16666.666667 $100,000+ 0.0 60 0.0 11.0 NaN Employed 91.0 True 740.0 759.0 16.0 16.0 7302.0 0.16 0.13636 0.1139 45898.0 A 6.0 23695 749.5
1017 0.04261 Current 4666.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Other 0.0 True 720.0 739.0 15.0 15.0 15954.0 0.25 0.07957 0.0659 29682.0 AA 7.0 15000 729.5
1085 0.04929 Current 2641.666667 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 42.0 True 740.0 759.0 13.0 12.0 4841.0 0.39 0.09434 0.0809 10894.0 AA 7.0 8500 749.5
1153 0.04806 Current 6250.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 75.0 True 700.0 719.0 19.0 19.0 3893.0 0.18 0.09030 0.0769 11470.0 AA 7.0 8000 709.5
1460 0.06663 Current 5009.750000 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 131.0 False 680.0 699.0 10.0 9.0 31072.0 0.24 0.13799 0.1099 5121.0 A 6.0 8000 689.5
1842 0.04260 Current 8608.250000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 45.0 True 780.0 799.0 20.0 20.0 14531.0 0.26 0.06726 0.0605 112969.0 AA 7.0 10000 789.5
1892 0.06081 Current 7500.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 100.0 True 680.0 699.0 11.0 11.0 22174.0 0.21 0.13189 0.1039 14199.0 A 6.0 20000 689.5
1962 0.06663 Current 4000.000000 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 120.0 True 720.0 739.0 9.0 7.0 9421.0 0.28 0.13227 0.1099 22010.0 A 6.0 14326 729.5
1972 0.07689 Current 2500.000000 $25,000-49,999 0.0 36 0.0 11.0 NaN Self-employed 173.0 True 660.0 679.0 11.0 11.0 7841.0 NaN 0.17090 0.1349 21163.0 B 5.0 4000 669.5
2048 0.04554 Current 10416.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 140.0 True 740.0 759.0 11.0 11.0 42965.0 0.16 0.08496 0.0716 42611.0 AA 7.0 13000 749.5
2097 0.06706 Current 16750.000000 $100,000+ 0.0 60 0.0 11.0 NaN Employed 53.0 True 700.0 719.0 9.0 8.0 60618.0 0.15 0.14147 0.1189 5568.0 A 6.0 25000 709.5
2181 0.08223 Current 8750.000000 $100,000+ 0.0 60 0.0 11.0 NaN Employed 39.0 True 680.0 699.0 19.0 17.0 73564.0 0.37 0.21166 0.1875 33116.0 C 4.0 4500 689.5
2288 0.05879 Current 11250.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 23.0 True 720.0 739.0 19.0 18.0 61476.0 0.25 0.12727 0.0990 74681.0 A 6.0 35000 729.5
2487 0.04260 Current 16666.666667 $100,000+ 0.0 36 0.0 11.0 NaN Self-employed 234.0 True 760.0 779.0 19.0 17.0 23274.0 NaN 0.06726 0.0605 65726.0 AA 7.0 15000 769.5
2548 0.04260 Current 8916.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 101.0 True 800.0 819.0 10.0 8.0 5627.0 0.11 0.06726 0.0605 21415.0 AA 7.0 5000 809.5
2550 0.04529 Current 9583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 49.0 True 780.0 799.0 8.0 6.0 5898.0 0.11 0.07922 0.0659 8203.0 AA 7.0 12000 789.5
2644 0.06499 Current 5833.333333 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 12.0 False 700.0 719.0 10.0 10.0 23490.0 0.27 0.13636 0.1139 24097.0 A 6.0 20000 709.5
2913 0.07077 Current 6833.333333 $75,000-99,999 0.0 60 0.0 11.0 NaN Employed 13.0 False 680.0 699.0 7.0 7.0 5076.0 0.08 0.15425 0.1314 40224.0 B 5.0 5000 689.5
3006 0.06663 Current 6916.666667 $75,000-99,999 12.0 36 0.0 11.0 NaN Employed 421.0 False 680.0 699.0 5.0 3.0 726.0 0.11 0.13799 0.1099 1274.0 A 6.0 5000 689.5
3067 0.04554 Current 9583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 243.0 True 740.0 759.0 11.0 11.0 69386.0 0.15 0.08496 0.0716 28768.0 AA 7.0 15000 749.5
3091 0.04261 Current 6250.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 14.0 False 780.0 799.0 11.0 11.0 10889.0 0.30 0.06726 0.0605 16111.0 AA 7.0 12000 789.5
3136 0.07824 Current 2114.916667 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 49.0 False 660.0 679.0 11.0 9.0 4307.0 0.48 0.20524 0.1685 8793.0 C 4.0 4000 669.5
3156 0.04929 Current 5333.333333 $50,000-74,999 0.0 36 577.0 11.0 NaN Employed 173.0 False 700.0 719.0 11.0 11.0 23777.0 0.25 0.09434 0.0809 64165.0 AA 7.0 15000 709.5
3189 0.07078 Current 11666.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 287.0 True 720.0 739.0 10.0 10.0 49829.0 0.23 0.14816 0.1199 33971.0 A 6.0 35000 729.5
3356 0.08268 Current 4333.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 27.0 False 680.0 699.0 10.0 10.0 16574.0 0.42 0.19501 0.1585 13526.0 B 5.0 11000 689.5
3371 0.04261 Current 8916.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 122.0 True 780.0 799.0 19.0 9.0 21478.0 0.17 0.06726 0.0605 83922.0 AA 7.0 18000 789.5
3474 0.06462 Current 7083.333333 $75,000-99,999 15.0 36 0.0 11.0 NaN Employed 6.0 False 680.0 699.0 5.0 5.0 1387.0 0.12 0.13301 0.1050 3363.0 A 6.0 10000 689.5
3519 0.04261 Current 7879.500000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 30.0 False 780.0 799.0 9.0 9.0 2685.0 0.11 0.06726 0.0605 34739.0 AA 7.0 6000 789.5
3594 0.04929 Current 2317.416667 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 124.0 False 800.0 819.0 6.0 6.0 166.0 0.10 0.09367 0.0809 55534.0 AA 7.0 4000 809.5
3614 0.05568 Current 5083.333333 $50,000-74,999 0.0 60 0.0 11.0 NaN Self-employed 36.0 True 740.0 759.0 11.0 11.0 17205.0 NaN 0.11501 0.0930 19595.0 A 6.0 10000 749.5
3646 0.04260 Current 9583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 32.0 False 760.0 779.0 6.0 6.0 4091.0 0.15 0.06726 0.0605 32409.0 AA 7.0 18000 769.5
3802 0.05047 Current 4583.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 243.0 True 760.0 779.0 15.0 14.0 28213.0 0.27 0.09000 0.0766 52654.0 AA 7.0 9000 769.5
3929 0.04929 Current 9666.666667 $100,000+ 1.0 60 0.0 11.0 NaN Employed 294.0 True 760.0 779.0 13.0 12.0 4482.0 0.10 0.09390 0.0809 28568.0 AA 7.0 18000 769.5
4020 0.07077 Current 8666.666667 $100,000+ 11.0 60 0.0 11.0 NaN Employed 75.0 True 700.0 719.0 18.0 18.0 8060.0 0.30 0.15449 0.1314 5415.0 B 5.0 15000 709.5
4068 0.07487 Current 5166.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 156.0 False 700.0 719.0 9.0 9.0 15000.0 0.36 0.15833 0.1299 2112.0 A 6.0 15000 709.5
4094 0.06026 Current 12500.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 49.0 True 760.0 779.0 13.0 10.0 1222.0 0.18 0.12274 0.0949 73258.0 A 6.0 34999 769.5
4472 0.07192 Current 3583.333333 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 198.0 False 680.0 699.0 9.0 6.0 5100.0 0.22 0.15844 0.1355 6008.0 B 5.0 10000 689.5
4631 0.04260 Current 7083.333333 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 128.0 True 800.0 819.0 9.0 8.0 7439.0 0.10 0.06726 0.0605 28861.0 AA 7.0 13620 809.5
4689 0.06968 Current 8225.000000 $75,000-99,999 29.0 36 3322.0 11.0 NaN Employed 33.0 True 640.0 659.0 5.0 3.0 662.0 0.07 0.14409 0.1159 96.0 A 6.0 5000 649.5
4696 0.07078 Current 13333.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 64.0 True 780.0 799.0 11.0 11.0 3827.0 0.10 0.14249 0.1199 24917.0 A 6.0 35000 789.5
4718 0.06081 Current 5833.333333 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 7.0 True 820.0 839.0 16.0 13.0 301.0 0.40 0.12637 0.1039 8199.0 A 6.0 15000 829.5
5111 0.06663 Current 5166.666667 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 322.0 True 740.0 759.0 15.0 11.0 21234.0 0.19 0.13227 0.1099 29443.0 A 6.0 10000 749.5
5112 0.04929 Current 6000.000000 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 133.0 True 700.0 719.0 9.0 7.0 113.0 0.12 0.09434 0.0809 7607.0 AA 7.0 3500 709.5
5188 0.04260 Current 9583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 27.0 False 800.0 819.0 9.0 8.0 1010.0 0.09 0.06726 0.0605 41490.0 AA 7.0 18000 809.5
5353 0.04261 Current 3333.333333 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 28.0 True 760.0 779.0 17.0 17.0 701.0 0.17 0.06726 0.0605 8299.0 AA 7.0 10000 769.5
5391 0.06663 FinalPaymentInProgress 5250.000000 $50,000-74,999 9.0 36 1432.0 11.0 NaN Employed 117.0 True 660.0 679.0 4.0 3.0 123.0 0.05 0.13799 0.1099 377.0 A 6.0 2000 669.5
5483 0.05879 Current 35000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 155.0 True 740.0 759.0 18.0 18.0 114938.0 0.11 0.12691 0.0990 149162.0 A 6.0 35000 749.5
5517 0.05568 Current 4666.666667 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 315.0 True 740.0 759.0 16.0 14.0 8828.0 0.21 0.11501 0.0930 22517.0 A 6.0 13000 749.5
5583 0.06388 Current 8666.666667 $100,000+ 11.0 36 0.0 11.0 NaN Self-employed 128.0 False 680.0 699.0 6.0 5.0 27634.0 NaN 0.13799 0.1099 18667.0 A 6.0 15000 689.5
5592 0.05047 Current 9500.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 40.0 True 740.0 759.0 16.0 15.0 30865.0 0.24 0.09000 0.0766 86814.0 AA 7.0 20000 749.5
5605 0.05879 Current 9333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 52.0 False 740.0 759.0 7.0 6.0 20390.0 0.13 0.12727 0.0990 38410.0 A 6.0 26000 749.5
5618 0.04929 Current 7000.000000 $75,000-99,999 0.0 60 0.0 11.0 NaN Employed 144.0 True 800.0 819.0 11.0 10.0 1024.0 0.08 0.09390 0.0809 44611.0 AA 7.0 8000 809.5
5636 0.05516 Current 10000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 200.0 True 740.0 759.0 20.0 20.0 40423.0 0.39 0.10038 0.0869 34481.0 AA 7.0 15000 749.5
5675 0.04231 Current 7084.833333 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 71.0 True 740.0 759.0 15.0 12.0 24510.0 0.27 0.07620 0.0629 77390.0 AA 7.0 20000 749.5
5929 0.06462 Current 10000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 115.0 False 700.0 719.0 15.0 13.0 28134.0 0.26 0.13301 0.1050 7163.0 A 6.0 3300 709.5
6047 0.04261 Current 2500.000000 $25,000-49,999 0.0 36 0.0 11.0 NaN Self-employed 79.0 False 800.0 819.0 10.0 10.0 2564.0 NaN 0.06726 0.0605 25836.0 AA 7.0 4000 809.5
6101 0.04261 Current 5000.000000 $50,000-74,999 0.0 36 0.0 11.0 NaN Self-employed 14.0 True 760.0 779.0 12.0 12.0 5275.0 NaN 0.06726 0.0605 63825.0 AA 7.0 8500 769.5
6196 0.05047 Current 4166.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Other 209.0 True 720.0 739.0 16.0 14.0 4970.0 0.28 0.09000 0.0766 25036.0 AA 7.0 15000 729.5
6256 0.04806 Current 14166.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 31.0 True 700.0 719.0 20.0 20.0 65991.0 0.13 0.09030 0.0769 3551.0 AA 7.0 20000 709.5
6411 0.04261 Current 4166.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 112.0 True 720.0 739.0 15.0 14.0 29234.0 0.27 0.07922 0.0659 73716.0 AA 7.0 15000 729.5
6430 0.04529 Current 8208.333333 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 63.0 False 780.0 799.0 10.0 10.0 3863.0 0.08 0.07922 0.0659 71137.0 AA 7.0 2000 789.5
6481 0.04554 Current 5333.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 72.0 True 720.0 739.0 15.0 15.0 14935.0 0.25 0.08496 0.0716 91565.0 AA 7.0 12500 729.5
6644 0.04929 Current 3750.000000 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 78.0 True 820.0 839.0 9.0 9.0 339.0 0.25 0.09390 0.0809 26061.0 AA 7.0 10000 829.5
6652 0.06910 Current 5416.666667 $50,000-74,999 0.0 60 0.0 11.0 NaN Other 78.0 True 720.0 739.0 17.0 17.0 82711.0 0.36 0.14658 0.1239 87734.0 A 6.0 12500 729.5
6704 0.04231 Current 7333.333333 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 210.0 False 740.0 759.0 12.0 6.0 201.0 0.21 0.07620 0.0629 12999.0 AA 7.0 20000 749.5
6788 0.05516 Current 10000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 141.0 False 800.0 819.0 9.0 9.0 5937.0 0.16 0.10038 0.0869 33844.0 AA 7.0 30000 809.5
6856 0.05516 Current 23333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 60.0 True 780.0 799.0 22.0 20.0 22856.0 0.14 0.10038 0.0869 137596.0 AA 7.0 35000 789.5
6862 0.04260 Current 5000.000000 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 168.0 True 780.0 799.0 8.0 7.0 13529.0 0.17 0.06726 0.0605 27771.0 AA 7.0 11000 789.5
6899 0.04261 Current 3333.333333 $25,000-49,999 0.0 36 0.0 11.0 NaN Self-employed 24.0 True 760.0 779.0 8.0 6.0 3424.0 NaN 0.06726 0.0605 16076.0 AA 7.0 3000 769.5
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7070 0.06153 Current 3300.833333 $25,000-49,999 0.0 36 0.0 11.0 NaN Full-time 107.0 False 700.0 719.0 11.0 11.0 3997.0 NaN 0.12691 0.0990 3788.0 A 6.0 2000 709.5
7109 0.04261 Current 10833.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 34.0 True 780.0 799.0 11.0 11.0 12775.0 0.18 0.06726 0.0605 51625.0 AA 7.0 12000 789.5
7115 0.06499 Current 4583.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 45.0 False 660.0 679.0 7.0 7.0 756.0 0.13 0.14206 0.1139 1494.0 A 6.0 5000 669.5
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8126 0.04806 Current 20833.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 37.0 False 720.0 739.0 14.0 14.0 34720.0 0.18 0.09065 0.0769 15448.0 AA 7.0 20000 729.5
8156 0.04261 Current 10000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 156.0 True 800.0 819.0 12.0 12.0 5469.0 0.30 0.06726 0.0605 20731.0 AA 7.0 20000 809.5
8219 0.06153 Current 7916.666667 $75,000-99,999 0.0 36 0.0 11.0 NaN Self-employed 125.0 True 700.0 719.0 19.0 18.0 25952.0 NaN 0.12691 0.0990 28537.0 A 6.0 3500 709.5
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110070 0.06499 Current 8000.000000 $75,000-99,999 8.0 36 1792.0 11.0 NaN Employed 20.0 False 660.0 679.0 10.0 9.0 13237.0 0.18 0.14243 0.1139 21563.0 A 6.0 12750 669.5
110155 0.04929 Current 3333.333333 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 189.0 True 700.0 719.0 16.0 16.0 16096.0 0.23 0.09434 0.0809 140032.0 AA 7.0 8500 709.5
110173 0.05516 Current 4358.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Other 0.0 True 740.0 759.0 13.0 12.0 22276.0 0.20 0.10038 0.0869 67088.0 AA 7.0 11000 749.5
110201 0.04806 Current 7916.666667 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 413.0 True 700.0 719.0 13.0 13.0 22568.0 0.21 0.09030 0.0769 9548.0 AA 7.0 10000 709.5
110438 0.04261 Current 7500.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 352.0 False 800.0 819.0 12.0 11.0 5451.0 0.10 0.06726 0.0605 51920.0 AA 7.0 5000 809.5
110458 0.04261 Current 7500.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 70.0 False 780.0 799.0 16.0 16.0 7298.0 0.21 0.07922 0.0659 54690.0 AA 7.0 6500 789.5
110589 0.04261 Current 9416.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 18.0 True 700.0 719.0 9.0 8.0 23887.0 0.12 0.07957 0.0659 28513.0 AA 7.0 20000 709.5
110680 0.04231 Current 10000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 84.0 True 740.0 759.0 19.0 18.0 21247.0 0.13 0.07620 0.0629 86853.0 AA 7.0 2000 749.5
110806 0.05298 Current 8083.333333 $75,000-99,999 8.0 36 32969.0 11.0 NaN Employed 51.0 False 700.0 719.0 8.0 8.0 5670.0 0.09 0.09534 0.0819 8618.0 AA 7.0 3500 709.5
110886 0.04261 Current 7750.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 81.0 False 780.0 799.0 7.0 7.0 8286.0 0.15 0.06726 0.0605 52099.0 AA 7.0 12000 789.5
110891 0.06388 Current 3750.000000 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 170.0 True 700.0 719.0 8.0 8.0 12569.0 0.20 0.13227 0.1099 3203.0 A 6.0 5000 709.5
111154 0.04929 Current 16250.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 165.0 False 720.0 739.0 11.0 11.0 77272.0 0.15 0.09434 0.0809 50378.0 AA 7.0 16000 729.5
111349 0.04929 Current 6458.333333 $75,000-99,999 0.0 60 0.0 11.0 NaN Employed 184.0 True 800.0 819.0 26.0 17.0 2637.0 0.25 0.09367 0.0809 37747.0 AA 7.0 11500 809.5
111448 0.06663 Current 6250.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 61.0 False 660.0 679.0 9.0 6.0 11188.0 0.16 0.13799 0.1099 3812.0 A 6.0 12000 669.5
111464 0.04656 Current 13333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 272.0 True 760.0 779.0 12.0 12.0 24339.0 0.13 0.08325 0.0699 66411.0 AA 7.0 20000 769.5
111468 0.06462 Completed 9583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 99.0 True 700.0 719.0 13.0 12.0 29250.0 0.30 0.13301 0.1050 20550.0 A 6.0 13696 709.5
111481 0.04806 Current 11283.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 68.0 True 720.0 739.0 12.0 10.0 6316.0 0.09 0.09065 0.0769 12679.0 AA 7.0 6001 729.5
111635 0.04261 Current 11250.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 19.0 True 780.0 799.0 15.0 15.0 34496.0 0.16 0.06726 0.0605 52660.0 AA 7.0 20000 789.5
111637 0.04806 Current 6250.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 56.0 True 720.0 739.0 14.0 11.0 11634.0 0.26 0.09065 0.0769 15666.0 AA 7.0 12000 729.5
111771 0.04231 Current 4583.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 116.0 True 740.0 759.0 11.0 10.0 85438.0 0.22 0.07620 0.0629 23068.0 AA 7.0 2000 749.5
111890 0.08610 Current 4166.666667 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 145.0 True 780.0 799.0 25.0 25.0 24923.0 0.29 0.17061 0.1474 62534.0 B 5.0 15000 789.5
111938 0.06970 Current 3416.666667 $25,000-49,999 0.0 60 0.0 11.0 NaN Employed 88.0 False 720.0 739.0 13.0 12.0 29534.0 0.40 0.15016 0.1274 7387.0 B 5.0 10000 729.5
112024 0.04231 Current 6083.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 67.0 True 780.0 799.0 12.0 12.0 14432.0 0.16 0.07620 0.0629 45018.0 AA 7.0 3000 789.5
112098 0.05342 Current 5416.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 11.0 False 700.0 719.0 15.0 15.0 12369.0 0.18 0.11563 0.0879 27531.0 A 6.0 11000 709.5
112124 0.04529 Current 5833.333333 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 114.0 True 780.0 799.0 13.0 13.0 10342.0 0.25 0.07922 0.0659 65555.0 AA 7.0 20000 789.5
112316 0.04231 Current 8333.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 166.0 True 780.0 799.0 14.0 12.0 18664.0 0.25 0.07620 0.0629 77136.0 AA 7.0 20000 789.5
112549 0.06663 Current 12333.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 14.0 True 760.0 779.0 19.0 18.0 132276.0 0.16 0.13227 0.1099 51272.0 A 6.0 25000 769.5
112646 0.04529 Current 10833.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 58.0 False 740.0 759.0 21.0 20.0 16867.0 0.19 0.07922 0.0659 33972.0 AA 7.0 3500 749.5
112653 0.04554 Current 10767.500000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 16.0 True 740.0 759.0 11.0 11.0 44973.0 0.24 0.08531 0.0716 86327.0 AA 7.0 12000 749.5
112684 0.07890 Current 9100.000000 $100,000+ 0.0 60 0.0 11.0 NaN Employed 175.0 True 720.0 739.0 11.0 10.0 67492.0 0.24 0.16294 0.1399 7625.0 B 5.0 31750 729.5
112774 0.04261 Current 30000.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 265.0 True 720.0 739.0 10.0 10.0 62398.0 0.12 0.07922 0.0659 27402.0 AA 7.0 9000 729.5
112775 0.04929 Current 5833.333333 $50,000-74,999 0.0 60 0.0 11.0 NaN Employed 188.0 False 780.0 799.0 6.0 5.0 3739.0 0.17 0.09390 0.0809 9810.0 AA 7.0 15000 789.5
112808 0.04260 Current 7000.000000 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 92.0 True 780.0 799.0 10.0 10.0 20301.0 0.23 0.06726 0.0605 44999.0 AA 7.0 19240 789.5
112857 0.06968 Current 8333.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 162.0 False 700.0 719.0 5.0 3.0 19056.0 0.07 0.13840 0.1159 4544.0 A 6.0 6500 709.5
112909 0.06388 Current 5833.333333 $50,000-74,999 1.0 60 0.0 11.0 NaN Employed 79.0 False 700.0 719.0 7.0 6.0 5310.0 0.12 0.13227 0.1099 14175.0 A 6.0 8000 709.5
112921 0.04261 Current 31666.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 162.0 True 800.0 819.0 21.0 19.0 21961.0 0.07 0.06726 0.0605 135839.0 AA 7.0 19000 809.5
112990 0.06462 Current 6916.666667 $75,000-99,999 0.0 60 0.0 11.0 NaN Employed 11.0 False 760.0 779.0 10.0 4.0 258.0 0.14 0.12726 0.1050 2342.0 A 6.0 10192 769.5
113044 0.05568 Current 9166.666667 $100,000+ 0.0 60 0.0 11.0 NaN Employed 34.0 True 760.0 779.0 28.0 27.0 100506.0 0.17 0.11501 0.0930 127212.0 A 6.0 15000 769.5
113167 0.05568 Current 14166.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 6.0 True 740.0 759.0 22.0 17.0 20604.0 0.17 0.12117 0.0930 28484.0 A 6.0 35000 749.5
113248 0.06968 Current 2500.000000 $25,000-49,999 0.0 60 0.0 11.0 NaN Self-employed 41.0 False 700.0 719.0 5.0 4.0 5216.0 NaN 0.13840 0.1159 5584.0 A 6.0 4000 709.5
113406 0.04929 Current 11250.000000 $100,000+ 0.0 36 0.0 11.0 NaN Employed 11.0 False 720.0 739.0 6.0 4.0 151.0 0.04 0.09434 0.0809 25349.0 AA 7.0 6000 729.5
113570 0.04806 Current 12916.666667 $100,000+ 0.0 36 0.0 11.0 NaN Employed 240.0 True 720.0 739.0 17.0 17.0 75940.0 0.20 0.09030 0.0769 138635.0 AA 7.0 20000 729.5
113706 0.06663 Current 4416.666667 $50,000-74,999 0.0 36 0.0 11.0 NaN Employed 68.0 False 660.0 679.0 23.0 19.0 27110.0 0.35 0.13799 0.1099 24283.0 A 6.0 15000 669.5
113750 0.04806 Current 7416.666667 $75,000-99,999 0.0 36 0.0 11.0 NaN Employed 163.0 True 800.0 819.0 5.0 5.0 1515.0 0.13 0.09065 0.0769 13785.0 AA 7.0 22000 809.5
113762 0.06499 Current 9083.333333 $100,000+ 0.0 60 0.0 11.0 NaN Employed 41.0 True 740.0 759.0 10.0 9.0 10427.0 0.23 0.13636 0.1139 18673.0 A 6.0 25000 749.5
113784 0.06153 Current 8583.333333 $100,000+ 0.0 36 0.0 11.0 NaN Employed 158.0 True 720.0 739.0 10.0 10.0 105680.0 0.30 0.12691 0.0990 5830.0 A 6.0 15000 729.5
113855 0.06706 Current 6666.666667 $75,000-99,999 0.0 36 0.0 11.0 NaN Full-time 89.0 False 640.0 659.0 6.0 5.0 5584.0 0.07 0.14751 0.1189 3616.0 A 6.0 8500 649.5
113881 0.08524 Current 8166.666667 $75,000-99,999 23.0 60 8463.0 11.0 NaN Employed 37.0 True 660.0 679.0 12.0 10.0 5177.0 0.29 0.19681 0.1730 1123.0 C 4.0 19432 669.5
113912 0.04260 Current 2500.000000 $25,000-49,999 0.0 36 0.0 11.0 NaN Employed 102.0 False 800.0 819.0 10.0 10.0 1031.0 0.20 0.06726 0.0605 50669.0 AA 7.0 4000 809.5
In [31]:
#creating a subset and dropping such rows of data.
prosper_score_high = factors[(factors['ProsperScore']> 10)].index
prosper_score_high
#dropping such rows
factors.drop(prosper_score_high,inplace = True)
In [32]:
#testing drop
factors.query('ProsperScore > 10')
Out[32]:
EstimatedReturn LoanStatus StatedMonthlyIncome IncomeRange DelinquenciesLast7Years Term AmountDelinquent ProsperScore CreditGrade EmploymentStatus EmploymentStatusDuration IsBorrowerHomeowner CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (Alpha) ProsperRating (numeric) LoanOriginalAmount creditscore
In [33]:
#checking the plot after the drop
default_color = sb.color_palette()[3]
sb.countplot(data = factors, x = 'ProsperScore', color = default_color).set_title('Count of Prosper Score ')
Out[33]:
Text(0.5, 1.0, 'Count of Prosper Score ')

Delinquencies and amount deliquent¶

AmountDeliquent: Dollars delinquent at the time the credit profile was pulled. Delinquencieslast 7 years: Number of delinquencies in the past 7 years at the time the credit profile was pulled.

In [34]:
x=np.array([10000,20000,30000,40000,50000,60000,70000,80000,90000])
fig, ax = plt.subplots(nrows=1, figsize = [8,8])
default_color = sb.color_palette()[0]
sb.histplot(data =factors,x ='AmountDelinquent',color= default_color).set_title('Distribution of borrowers deliquent Amount')
plt.xlim(0,110000);

Observation:¶

A majority of the borrowers have a deliquent amount of 20,000 at the time the loan was applied for.There are a few outliers having up to 100000. But the most of the borrowers delinquent amount fall between 0-50000.

DelinquenciesLast7Years¶

Number of delinquencies in the past 7 years at the time the credit profile was pulled.

In [35]:
x=np.array([5,10,15,20,25,30,35,40,45,60,65,70,75])
plt.figure(figsize =(12,6))
plt.title('Distribution of borrowers Delinquencies in past 7years');
plt.hist(data= factors, x= 'DelinquenciesLast7Years')
plt.xlim(0,70)
plt.xticks(x);

OBSERVATION¶

Borrowers delinquencies in the last 7years is skewed towrards the right with the majority of the counts occuring between 5 and 10.We seem to be having some outliers around 65-70. The deliquent amount is skewed towards the right as well, as the amount increases the count of the deliquent amounts decreases. The skewedness of both columns makes sense because they should be closely linked with each other.

current credit lines¶

Number of current credit lines at the time the credit profile was pulled.

In [36]:
plt.subplots(figsize=[8,8])
plt.title('Distribution of Borrowers Current credit lines')
plt.hist(data = factors, x = 'CurrentCreditLines')
plt.xlabel('CurrentCreditLines')
plt.xlim(0,45);

Observation:¶

The frequency of current credit lines ranges between 0-35, with highest frequency in 10 and the lowest in 40.

Distribution of open credit line¶

In [37]:
plt.figure(figsize =(8,8))
x= np.array([5,10,15,20,25,30,35,40])
plt.hist(data = factors, x = 'OpenCreditLines');
plt.xticks(x)
plt.xlabel('Open credit lines')
plt.xlim(0,40)
plt.title('Distribution of open credit lines')
Out[37]:
Text(0.5, 1.0, 'Distribution of open credit lines')

Observation:¶

The majority of the open credit lines is skewed towards the left.

Revolvingcredit balance:¶

Dollars of revolving credit at the time the credit profile was pulled.

In [38]:
plt.figure(figsize=(10,5))
sb.histplot(data = factors, x = 'RevolvingCreditBalance',binwidth =3500)
plt.title('Distribution of Revolving credit balance')
plt.xlim(0,270000)
Out[38]:
(0.0, 270000.0)

Observation:¶

As expected, the revolving credit balance is skewed towards the right, which is a clear indication that the frequency of people with high revolving credit balance, reduces as the revolving credit balance increases.The majority of the clients revolving credit balance falls within 0-100,000 dollars, with a few outliers having up to around 230,000dollars.

Available bank credit¶

The total available credit via bank card at the time the credit profile was pulled.

In [39]:
plt.title("Distribution of Avaialable bank credit")
plt.hist(data = factors,x ='AvailableBankcardCredit')
plt.xlim(0,150000)
plt.xlabel('Available bank credit')
plt.ylabel('count of available bank credit')
Out[39]:
Text(0, 0.5, 'count of available bank credit')

Observation:¶

The range of available bank credit falls within 0-120,000.

INSIGHTS¶

1.What are the factors that affect a loans outcome status?¶

2.What factors affects the borrower APR?¶

3.Does the term of the loan affect the annual percentage rate(Borrower's APR) of the borrower?¶

Insight1.¶

What Factors affects a loan's outcome status?:¶

To answer this, a close examination of the factors with a strong correlation with prosperscore,and prosper rating will be explored.Other plots will be utilised as well. Prosper score and prosper rating are used because of they're positively strongly correalted with each other.

BIVARIATE EXPLORATION¶

General overview of correlation between variables¶

In [40]:
#categorising the data types into numeric and categorical data
num_vars = factors.select_dtypes(include='number').columns
cat_vars = ['Term', 'EmploymentStatus', 'ProsperRating (Alpha)','IsBorrowerHomeowner']
In [41]:
#Correlation between variables
## A heatmap is used to depict a genral overview of the correlartion between all the variables in the data set.
plt.figure(figsize = [10,6])
plt.title('Correlation of all variables')
sb.heatmap(factors[num_vars].corr(), annot = True, fmt = '.2f',
           cmap = 'RdBu',vmin=-1,vmax=1, center = 0)
plt.show()

The intensity of the color scale is meant to depict the level of correlation betweeen two variables,the darker the blue , the more positive the correlation and the deeper the red, the more negative the correlation between both variables. Neutral correlations almost have a white color.Which depicts that both variables have little to no correlation The numbers in the maps also depicts the level of correlation, the higher the number, the higher the positive correlation. For example credit score range lower and upper have a very strong negative correaltion with the credit score, borrowerAPR and borrower rate have a very strong negative correaltion with prosper rating, which makes sense, because the better a borrowers score, the lesser the interest rates should be.

The heatmap above displays the intensity of correlation the various variables have with each other. All the variables with either a strong positive or strong negative correlation with the prosper score and prosper rating can be said to affect the loan's status outcome. The most notable features are: 1.The creditscore- This has a strong positive correlation 2.Loan original Amount 3.Available bank credit 4.Borrower rate 5.Borrower APR Other features will be explored irrespective

In [42]:
#A plot to show how closely related the prosperscore and the prosper rating are.
sb.scatterplot(data=factors,x= 'ProsperScore',y='ProsperRating (Alpha)').set_title('Correlation of prosper score and prosper rating')
Out[42]:
Text(0.5, 1.0, 'Correlation of prosper score and prosper rating')

Creditscore and prosper score¶

In [43]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors,x= 'creditscore',y='ProsperScore');
plt.title("Correlation of prosper score and amounts requested for loans ");

Observation:¶

The both variables have a strong linear positive correlation.And this simply depicts that clients with higher creditscore tend to get higher prosperscore and stands a higher chance of getting their loans approved.

Is BorrowerHome owner or not¶

In [44]:
plt.figure(figsize = [10, 8])
sb.countplot(data=factors, x='ProsperRating (Alpha)', hue='IsBorrowerHomeowner', palette='Dark2');
plt.legend(loc=1);
plt.title("Count of home owning borrowers against Prosper rating ");

OBSERVATION:¶

It can be seen from the plot above that in the C,B,A and AA sub categories of the prosper rating,the borrowers with homes always had a higher count than others wthout home,for the last three ratings,the borrowers without home had a much higher count. The pattern observed was for borrowers without home,there was a steady incline up until the prosper rating B subacategory where a steady decline started and it can be seen that the lowest count for non home owners is in the highest prosper rating AA. It can also be seen that the AA prosper rating had the highest difference between home owners and non home owners.

Loan Original Amount and Prosper score¶

In [45]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors,y= 'LoanOriginalAmount',x='ProsperScore');
plt.title("Correlation of prosper score and amounts requested for loans ");

Observation:¶

There is a strong positive linear correlation between the both variables, as it's seen in the plot above as the prosper score increases, the amount of loan increases as well.

How employment Status Affects Prosper Rating¶

In [46]:
plt.figure(figsize = [10,8])
sb.countplot(data=factors, x='ProsperRating (Alpha)', hue='EmploymentStatus', palette='Set2');
plt.legend(loc=1);
plt.title("Common Employment Status in each Prosper Rating");

Observation:¶

It can be seen from the plot above that amongst all the sub categories in the prosper rating, the employed people always stood a better chance of getting better prosper scores.The fulltime sub category ranked the highest in the AA and A sub category of the prosper rating. The employment status plays a key role in determining the propser rating of a client.

How does the debt to income ratio affect the client's prosper score?¶

In [47]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors, y='ProsperScore', x='DebtToIncomeRatio');
plt.ylim(0,15)
plt.xlim(0,10)
plt.title("Correlation of prosperscore and DebtToincome Ratio");

Observation:¶

The plot above shows us that a higher debt to income ratio will result in a reduced prosperscore.The both variables have a strong negative linear correlation.

How Revolving Credit balance affects prosper score¶

In [48]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors, y='ProsperScore', x='RevolvingCreditBalance');
plt.title(" Correlation of Prosperscore and Revolving credit balance ");

Observation:¶

There is a positive correlation between the revolving credit balance and the prosperscore.Which is very unexpected,maybe it's because clients with a higher revolving credit balance means they have a track record of taking loans and stands a higher chance of paying back the loan.

Stated Monthly Income and Prosper score¶

In [51]:
plt.subplots()
sb.regplot(data=factors,x='StatedMonthlyIncome',y='ProsperScore')
plt.xlim(0,65000)
Out[51]:
(0.0, 65000.0)
In [52]:
#There is an outlier that's skewing the plot and i think it needs to be dropped

Observation:¶

The plot shows a positive linear correlation with the prosper score variable. This indicates that the monthly income plays affects the approval of a loan.

In [53]:
#fishing out the outlier
factors.StatedMonthlyIncome.max()
Out[53]:
1750002.916667
In [54]:
#Dropping the outlier
out_income = factors[(factors['StatedMonthlyIncome']== 1750002.916667)].index
#dropping such rows
factors.drop(out_income,inplace = True)
In [55]:
factors.query('StatedMonthlyIncome ==1750002.916667')
Out[55]:
EstimatedReturn LoanStatus StatedMonthlyIncome IncomeRange DelinquenciesLast7Years Term AmountDelinquent ProsperScore CreditGrade EmploymentStatus EmploymentStatusDuration IsBorrowerHomeowner CreditScoreRangeLower CreditScoreRangeUpper CurrentCreditLines OpenCreditLines RevolvingCreditBalance DebtToIncomeRatio BorrowerAPR BorrowerRate AvailableBankcardCredit ProsperRating (Alpha) ProsperRating (numeric) LoanOriginalAmount creditscore
In [56]:
#plotting after the drop
plt.subplots()
sb.regplot(data=factors,x='StatedMonthlyIncome',y='ProsperScore')
plt.xlim(0,65000)
plt.ylim(0,11)
Out[56]:
(0.0, 11.0)

Stated Monthly Income and Prosper score. and terms¶

In [57]:
sb.lineplot(data = factors, x ='ProsperScore',y='StatedMonthlyIncome').set_title('Correlation of Prosperscore and  StatedMonthly Income')
Out[57]:
Text(0.5, 1.0, 'Correlation of Prosperscore and  StatedMonthly Income')

Term and Prosper Score¶

In [58]:
plt.figure(figsize=(8,5))
sb.barplot(data=factors,x='Term',y='ProsperScore', palette='rainbow')
plt.title("Count of prosper score for the loan Terms")
Out[58]:
Text(0.5, 1.0, 'Count of prosper score for the loan Terms')

Observation:¶

The lowest loan term had the highest prosper score , but there was no particular pattern.

INSIGHT 1 SUMMARY FOR BIVARIATE ANALYSIS:¶

After exploring the features of the data set,certains features displayed a strong correlation using bivariate plots and countplots to examine the relationship between the prosper score and prosper rating. The features that showed a corrrelation are;

  1. Debt to income ratio: It was seen from the plot that the higher the debt to income ratio, the lower the client's prosper score.It had a strong linear correlation with the prosperscore.
  2. Creditscore: The credit score had a positive linear correlation with the prosper score.The higher the clients credit score, the higher their prosper score and rating, which means it increases their chances of getting their loan approved.
  3. Employment Status:Amongst the subcategories of employment status provided, the employed category had a higher count across all the prosper rating categories when a countplot was used, this indicates that the eployment status affects the prosper score.
  4. Is borrower Home owner: The availability of a home when applying for a loan also plays a role, from the plot it was seen that clients with a home had a higher prosper rating than the others without a home.
  5. Revolving credit Balance: The scatterplot of the revolving credit balance showed a positive linear correlation with the prosper score.This indicates that the higher the revolving credit balance, the higher the prosper score.
  6. StatedMonthly Income: The plot revealed that an increase in the monthly income causes an increase in the prosper score as well, hence it plays a role in determining the status outcome of a loan application.

Insight 2¶

What Affects the borrowers APR or interest rates¶

Interaction of of numeric and categorical features.¶

The heat map plotted earlier will be used to determine the features that affects the borrowers APR and borrower rate. By exploring all the features with a correlation to borrower APR. Most notable features from the heatmap are; 1.prosper rating 2.Prosper score 3.Credit score 4.Delnquencies in past 7years 5.Available bank credit 6.Loan Original Amount 7.Debt to income ratio

Borrower APR and Prosper score¶

In [59]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors, y='BorrowerAPR', x='ProsperScore');
plt.title("Correlation of Borrower APR and ProsperScore");
plt.ylim(0.04,0.42)
plt.xlim(0,11)
Out[59]:
(0.0, 11.0)

Observation:¶

A linear negative correlation can be observed from the plot above. As the prosper score increases, the interest rate(Borrower APR) charged also decreases.

Borrower APR and prosper rating¶

In [60]:
plt.figure(figsize=(12,10))
plt.subplot(2,2,1)
sb.boxenplot(data= factors,x='ProsperRating (Alpha)',y='BorrowerAPR').set_title('Correlation of prosper rating and Annual interest rates')
plt.subplot(2,2,2)
sb.boxenplot(data= factors,x='ProsperRating (Alpha)',y='BorrowerRate').set_title('Correlation of prosper rating and Annual interest rates')
Out[60]:
Text(0.5, 1.0, 'Correlation of prosper rating and Annual interest rates')

Observation:¶

The median values of the borrower APR is seen to decrease as the various sub categories of the prosper rating variable increases.A borrower with a good prosper score will most likely be charged less interest for their loans.

Borrower APR and DebtTo income ratio¶

In [61]:
plt.figure(figsize = [10, 8])
sb.regplot(data=factors, y='BorrowerAPR', x='DebtToIncomeRatio');
plt.ylim(0,0.6)
plt.xlim(0,12)
plt.title("Correlation of Borrower APR and DebtToincome Ratio");

Observation:¶

The plot above shows a positive linear correlation between the both variables. This indicates that as the debt to income ratio increases, the client will be charged a higher interest rate (APR) for their loans.

Borrower APR and Creditscore¶

How does the creditscore affect the borrower APR?¶

In [62]:
plt.figure(figsize=(8,6))
sb.regplot(data= factors, x='creditscore',y='BorrowerAPR')
plt.title("Correlation of creditscore and Borrower APR")
plt.xlabel('creditscore')
plt.ylabel('BorrowerAPR')
plt.xlim(350,950)# This was used to trim out an outlier that was an obvious error, because creditscores don't get that high.
Out[62]:
(350.0, 950.0)

Observation:¶

Generally as the creditscore goes up, the BorrowerApr tends to reduce, as can be seen from the plot above.This plot implies that the better the credit score, the lesser the interest rates to be charged on the loan.

BorrowerAPR and DelinquenciesLast7Years¶

In [63]:
plt.figure(figsize=(8,6))
sb.regplot(data= factors, y='BorrowerAPR',x='DelinquenciesLast7Years')
plt.title("Correlation of Borrower APR and DelinquenciesLast7Years")
plt.xlabel('DelinquenciesLast7Years')
plt.ylabel('BorrowerAPR')
Out[63]:
Text(0, 0.5, 'BorrowerAPR')

Observation:¶

The delinquencies in the past 7 years variable is seen to increase linearly with the borrower APR. This indicates that the higher the delinquncies, the higher the annual interest rate will be charged.

In [64]:
### Borrower APR and Available bank credit
In [65]:
plt.figure(figsize=(8,6))
sb.regplot(data= factors, y='BorrowerAPR',x='AvailableBankcardCredit',x_jitter=1,y_jitter=1.5)
plt.title("Correlation of Borrower APR and AvailableBankcardCredit")
plt.xlabel('AvailableBankcardCredit')
plt.ylabel('BorrowerAPR')
Out[65]:
Text(0, 0.5, 'BorrowerAPR')

Observation:¶

Available bank credit variable is seen to have a negative linear correlation with the Borrower APR, this implies that the higher the available bank credit card, the lesser the annual interest rates.

In [66]:
plt.figure(figsize=(8,6))
sb.regplot(data= factors, y='BorrowerAPR',x='LoanOriginalAmount',)
plt.title("Correlation of Borrower APR and Loan Original Amount")
plt.xlabel('LoanOriginalAmount')
plt.ylabel('BorrowerAPR')
Out[66]:
Text(0, 0.5, 'BorrowerAPR')

Does Employment Status affect the borrower APR?¶

In [67]:
plt.figure(figsize=(8,6))
default_color =sb.color_palette()[0]
sb.boxplot(data = factors,x='EmploymentStatus',y='BorrowerAPR',color=default_color).set_title('Borrower APR and Employment Status')
plt.xticks(rotation=45);

Observation:¶

Borrowers in the not employed category tend to be charged a higher interest than others who are employed. As seen from the plot above, the fulltime and part_time categories have the lowest borrower apr's, meanwhile the other category also have a higher borrower APR.

INSIGHT 2 SUMMARY FOR BIVARIATE ANALYSIS:¶

The variables in the data set that were explored and discovered to have an effect on the borrower APR includes;

  1. The creditscore
  2. The debt to income ratio
  3. The Available bank card credit
  4. Employment Status
  5. Prosper score
  6. Prosper rating
  7. Delinquencies in the last 7 years. They were all found to have either a positive or negative correlation with the borrower APR.

INSIGHT 3¶

Does the term of the loan affect the annual percentage rate(Borrower's APR) of the borrower?¶

In [68]:
plt.figure(figsize=(10,10))
plt.subplot(2,2,1)
sb.boxplot(data= factors, x='Term',y='BorrowerAPR')
plt.title('Correaltion of borrower APR and loan term')
plt.subplot(2,2,2)
sb.boxplot(data= factors, x='Term',y='BorrowerRate')
plt.title('correlation of Borrower rate and loan term')
Out[68]:
Text(0.5, 1.0, 'correlation of Borrower rate and loan term')
In [69]:
sb.barplot(data=factors, x='Term',y= 'BorrowerAPR').set_title('Loan Term Apr scores')
Out[69]:
Text(0.5, 1.0, 'Loan Term Apr scores')

Observation:¶

The 12 months sub category of the term variable had the highest APR, while the other two were of the same value. There was an increase in the rate from 12 to 36 months and from 36 to 60 months, there was no increase . This indicates that the term appers to have an effect on the borrower rate and not have a very significant effect on the annual interest rates that the client will be charged. In summary the loan term has a very little effect on the annual interest rates, as can be seen from the plots above.

MULTIVARIATE PLOTS¶

INSIGHT 1: Factors that affects a loan's Status Outcome¶

In [70]:
#Creating a custom function for box plot
def boxgrid(x, y, **Kwargs):
    default_color = sb.color_palette()[2]
    sb.boxplot(x, y, color = default_color)
In [71]:
# creating a custom function for boxen plot
def boxen(x,y, **Kwargs):
    default_color = sb.color_palette()[3]
    sb.boxenplot(x,y, color =default_color)
In [72]:
#Blocking the warnings temporarily
import warnings
warnings.filterwarnings('ignore')
In [73]:
#creation of pairgrid using boxplots
g = sb.PairGrid(factors,y_vars = ['BorrowerRate','BorrowerAPR','LoanOriginalAmount','AvailableBankcardCredit','DebtToIncomeRatio',
                                 'creditscore'], 
                                x_vars = ['Term', 'ProsperRating (Alpha)', 'EmploymentStatus'], height = 3.5, aspect = 1.0)
g.map(boxgrid);
plt.xticks(rotation=45);
g.fig.suptitle('Term,EmploymentStatus & ProsperRating Vs Numerical features');
plt.show();
In [74]:
g=sb.PairGrid(factors,y_vars=['BorrowerRate','BorrowerAPR','LoanOriginalAmount','AvailableBankcardCredit','DebtToIncomeRatio',
                                 'creditscore'], 
                                x_vars = ['Term', 'ProsperRating (Alpha)', 'EmploymentStatus'], height = 3.5, aspect = 1.0)
g.map(boxen);
plt.xticks(rotation=90);
g.fig.suptitle('Term,EmploymentStatus & ProsperRating Vs Numerical features');
plt.show();

OBSERVATION:¶

Loan Original amount and Term:¶

The shortest loan term had the lowest original loan amount and the longest loan term had the highest loan original amount.This indicates that loan original amount have a positive linear correlation with the loan term. The bigger the amount, the longer the term will be offered to pay the loan.

Available bank credit and prosper rating¶

The AA category was seen to have the highest available bank credit. And the lowest prosper rating had the lowest available bank credit.This indicates that the bank credit plays a role in determining the prosper rating.

BorrowerRate and Prosper rating¶

As can be seen for the plot above, there is a negative correlation between the propser rating and the borrower rate, the higher the prosper rating, the lesser the interest rates which will the customer will be charged.

Borrower rate and term¶

The borrower rate and Term have a bit of a positive correlation.It can be seen that as the loan term(repayment time) increases, the borrower rate increases.

Prosper score and Term¶

Prosperscore and Term have a bit of a negative relationship with each other, it can be seen that the higher a borrowers propser score is, the more time will be given to repay their loans.

Employment status and BorrowerRate:¶

Amongst the sub categories of employment status it can be seen that not employed category have the highest borrower rate, and the lowest borrower rates are for the fulltime and part time clients.It makes sense for full time workers to have a lesser interest rate charged, because they tend to have a higher chance of paying back , because of a steady income stream.

Employmentstatus and Prosperscore:¶

Fulltime category have the highest prosperscore, followed by the parttime category and the retired category,the employed category have the third highest prosper score.

Creditscore and Term:¶

The plot reveals that the credit score does not seem to have an effect on the loan's term.The 60months and 12 months term , which are the smallest and the biggest happen to have approximately the same median values for creditscore.

Credit score and Prosper rating:¶

There is a steady incline for the prosper rating as the credit score goes up,except for the HR and D sub categories of prosper rating. The credit score can be said to have a strong positive correlation with prosper rating. Creditscore.

Credit score and employment Status:¶

The employed category had the highest credit score, followed next by the self employed category.The not employed category had the third highest credit score, it was higher than the full time and part time sub categories. This came as a surprise.

Debt to income ratio and term:¶

The median values for all three loan term were the same. This depicts a zero correlation with both variables.

Debt to income ratio and prosper score:¶

There was a decline in the debt to income ratio as the propser score increased.This indicates thatthe debt to income ratio had a negative correlation with the prosper rating

Debt to income ratio and employment status:¶

The not employed sub category happens to have the highest debt to income ratio and the employed category have the lowest.

In [75]:
### Effect of employment status on loan term and borrower APR.
In [78]:
plt.figure(figsize=(27,12))
plt.subplot(2,2,1)
fg=sb.violinplot(x='Term',y="BorrowerAPR",data=factors, hue='EmploymentStatus', palette='rainbow').set_title('Effect of employment Status on Borrower APR and Loan Term')
plt.show()

Observation:¶

As seen from the plot,the part time category had the lowest interest rates charged , followed by the full time category in the 36months term.The not employed category had the highestinterest rates(Borrower APR). Meanwhile for the 60 months term, a different pattern is seen.The retired category had the lowest APR followed by the employed category. For the 12 months term, the not employed catrgory had the highest APR. The plot is not good enough to conclude if the employment status affects the APR.Further analysis needs to be carried out.

Effect of loan term on borrower rate and Prosper rating¶

In [79]:
sb.pointplot(data=factors, x='ProsperRating (Alpha)', y='BorrowerRate', hue='Term',
                   palette='tab10');
plt.legend(loc=1, title='Employment Status');
plt.title('Effect of loan term on prosper rating and borrower APR');
plt.ylabel(' Borrower Rate');

Observation:¶

For all three loan terms, there is a steady decline and a negative correlation between borrower rate and propser rating.As the prosper raing increases, the borrower rate decreases. Across the three loan terms,a longer or higher loan term tends to attract a higher borrower rate/interest rate.This does not come as a surprise because a longer loan term results in a higher risk, and hence the company would want to be compensated for taking higher risks on the clients.It could also be as a result of accumulation of daily interest rates as well.

Effect of employment status on prosper rating and borrower APR¶

In [80]:
# Create a pointplot to show how the BorrowerRate changes for different loan Terms
# when split up by ProsperRating
plt.figure(figsize=[8,6])

sb.pointplot(data=factors, x='ProsperRating (Alpha)', y='BorrowerRate', hue='EmploymentStatus',
                   palette='tab10');
plt.legend(loc=1, title='Employment Status');
plt.title('Effect of employment status on prosper rating and borrower APR');
plt.ylabel(' Borrower Rate');

Observation:¶

For all the employmnet status, there is a steady decline and a negative correlation between borrower rate and propser rating.As the prosper raing increases, the borrower rate decreases. The unemployed borrowers are charged higher interest rates from the D subcategory to the AA sub category.The part time category was charged the highest rates for the HR category and the retired people were charged highest rates for the E category of prosper rating.

In [ ]:
!jupyter nbconvert Explanatory_Loans_Viz.ipynb --to slides --post serve --no-input --no-prompt

SUMMARY:¶

INSIGHT 1:¶

After exploring the dataset, I observed that there are certain key factors that affects/influences if a borrower will get their loan approved or not and how much interest they will be charged for their loans. By examining Certain key performance indicators such as the prosperscore, prosper rating. I was able to observe how certain others factors influences/affetcs the prosper score, prosper rating. The following factors below was observed to affect the KPI's: 1.Credit score 2.Revolving credit balance 3.Stated monthly income 4.Debt to income ratio 5.Avaialble bank credit. 6.Is borrower home owner or not The creditscore have a strong negative correlation with the borrower APR and Borrower rate, the plots showed us that the higher the credit score, the lesser the borrower APR and borrower rate charged on the loan collected by the borrower. Credit score also had a strong positive correlation with prosper rating and propser score, this did not come as a surprise. Surprisingly revolving credit balance was found to have a positive correlation with the prosper score. Maybe it's because a higher revolving credit balance is indicative of the borrower's consistency in paying back their loans and having a good track record. I also found out that borrowers with higher prosper rating tends to have a better monthly income.The both variables were found to have a positive correlation. Debt to income ratio was negatively correlated with the prosper score, the plots indicated that a higher debt to income ratio will result in a lower prosper score and this ultimately affects if the loan will be approved for the client or not. Available bank credit also plays an important role in determining the borrowers prosper score and prosper rating.It was seen to have a strong positive correlation with the loan original amount, and the creditscore as well.The higher the available bank credit, the higher the prosper rating and score. It was surprising to see the data indicate that clients with a high propser rating will be charged a higher interest rate(borrower APR) with a higher loan amount, it was a bit shocking, but made alot of sense. Borrower Rate and prosper rating have a strong negative correlation,the better the propser rating, the lesser the interest rates(borrower APR). Owning a home while applying for a loan affects the outcome of the loan. It was seen via the plots that homeowners had higher counts in the top four prosper rating categories.

INSIGHT 2:¶

The following factors were found to have a strong correlation with borrower APR; 1.prosper rating 2.Prosper score 3.Credit score 4.Delinquencies in past 7years 5.Available bank credit 6.Loan Original Amount 7.Debt to income ratio The factors above either had a negative or positive correlation with borrower APR.

INSIGHT 3:¶

The 12 months sub category of the term variable had the highest APR, while the other two were of the same value. There was an increase in the rate from 12 to 36 months and from 36 to 60 months, there was no increase . This indicates that the term appers to have an effect on the borrower rate and not have a very significant effect on the annual interest rates that the client will be charged. In summary the loan term has a very little effect on the annual APR , but a slightly stronger effect on interest rates.

References¶

https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/ https://www.geeksforgeeks.org/how-to-set-a-seaborn-chart-figure-size/ https://www.geeksforgeeks.org/how-to-set-the-spacing-between-subplots-in-matplotlib-in-python/ https://stackoverflow.com/questions/42528921/how-to-prevent-overlapping-x-axis-labels-in-sns-countplot https://archive.is/LHKqy https://archive.is/df2uH https://julienbeaulieu.gitbook.io/wiki/sciences/programming/data-analysis/data-visualization/bivariate-exploration-of-data/overplotting-transparency-and-jitter https://github.com/Abdelrhman2022/Loan-Data-From-Prosper/blob/main/LoanDatafromProsper.ipynb https://github.com/yduan004/communicate-data-loan-data-from-Prosper/blob/master/slide_deck.ipynb

Key Insights For Presentation¶

For the presentation, i will be looking at what factors affects outcome of a loan's status, to see if it will be accepted or rejected, I will also take a look at if the loan term affetcs the annual percentage rate of the borrower. I focused on factors that had either a strong positive or negative correlation with the prosper score and prosper rating . I started by examining the distribution of variables and afterwhich a heatmap was used to get a general overview of the correlation of both categoricl and numerical variables. After which different kinds of plot was used to get a clearer relationship and correlation between variables that affects the prosper score and ratings.I also used a plot to display the effect of loan term on prosper rating and borrower APR.

In [ ]: